Molecular and Functional Profiling of Gαi as an Intracellular pH Sensor

Heterotrimeric G proteins (Gα, Gβ and Gγ) act downstream of G-protein-coupled receptors (GPCRs) to mediate signaling pathways that regulate various physiological processes and human disease conditions. Previously, human Gαi and its yeast homolog Gpa1 have been reported to function as intracellular pH sensors, yet the pH sensing capabilities of Gαi and the underlying mechanism remain to be established. Herein, we identify a pH sensing network within Gαi, and evaluate the consequences of pH modulation on the structure and stability of the G-protein. We find that changes over the physiological pH range significantly alter the structure and stability of Gαi-GDP, with the protein undergoing a disorder-to-order transition as the pH is raised from 6.8 to 7.5. Further, we find that modulation of intracellular pH in HEK293 cells regulates Gαi-Gβγ release. Identification of key residues in the pH-sensing network allowed the generation of low pH mimetics that attenuate Gαi-Gβγ release. Our findings, taken together, indicate that pH-dependent structural changes in Gαi alter the agonist-mediated Gβγ dissociation necessary for proper signaling.


Introduction
Within the complex milieu of living cells, intracellular pH (pHi) is maintained within a narrow range, as even small changes in pH can affect a myriad of cellular processes including membrane potential, ion transport, cellular growth and metabolism 1,2,3 .Unsurprisingly, disruptions in pH regulation can contribute to the development of pathological conditions such as ischemic heart disease, cancer, and neurological disorders 3,4 .While various proton pumps and transporters play a role in regulating the flow of protons across the membrane to uphold pHi homeostasis, there are intracellular proteins (termed pH sensors) that sense and transmit pH signals, thus orchestrating the regulation of biochemical processes.Among these pH sensors are signal-transducing proteins.
Notably, a study by Isom et.al. (2013) provided evidence that a subset of signal-transducing heterotrimeric G proteins may serve as intracellular pH sensors 3 .These membrane-associated proteins form a heterotrimeric complex (Gα, Gβ and Gγ) and act downstream of G-protein coupled receptors (GPCRs).GPCRs represent the most extensive group of membrane proteins that are targeted by approved drugs.They play a crucial role in orchestrating the majority of cellular responses to hormones and neurotransmitters through several signaling pathways mediated by different isoforms of G protein (Gαi, Gαs, Gα12/13, and Gαq) that receive and transduce signals through diverse pathways 5,6 .The Gβ and Gγ subunits associate to form a Gβγ heterodimer 3 whereas the Gα subunit binds GDP or GTP and catalyzes GTP hydrolysis.The Gα subunit is comprised of two distinct domains: a helical domain and a Ras-like domain.Within the Ras-like domain, there are three key 'Switch' regions, namely SW-I, SW-II, and SW-III, which play an essential role in its activity (Fig. 2D).In the GDP-bound state, which is an inactive form of Gα protein, these Switch regions exhibit dynamic behavior.In contrast, in the GTP-bound state (the active form of Gα), they become more structured and less dynamic 7 .In the GDP-bound state, the Gα subunit is associated with the Gβγ complex.Upon GPCR stimulated Gα GTP-loading, the Gβγ subunits dissociate from Gα-GTP and along with G promote activation of downstream signaling pathways.Isom et.al. (2013) developed a computer algorithm, pHinder, which predicted that both the mammalian Gi isoform and yeast homolog Gpa1 contain a core of residues between the Raslike and helical domains that may promote pH sensing properties 8 .In support of this prediction, both proteins showed pH-dependent changes in thermostability over a pH range from 5.5 to 8.
They also found that Gpa1 undergoes phosphorylation under acidic conditions to attenuate pheromone-dependent stimulation of mitogen-activated protein kinases in the yeast 4 .While these findings, taken together suggest that mammalian Gαi may function as a pH sensor, the molecular mechanism, and the biological consequence of pH sensing through Gαi remain unknown.
To expand on past observations, we characterized pH-dependent biochemical and structural/dynamic properties of GDP-bound Gαi, identified the underlying pH-dependent electrostatic network, and determined functional consequences on cellular Gαi activity.We find that the structure, stability, and dynamics of Gαi in the GDP-bound state are highly dependent on pH over the physiological pH range due to a pH-responsive network within the Ras-like domain.
These findings differ from a previous report where larger pH-dependent changes in thermostability were observed for the GTP-bound state with ionizable residues predicted to lie at the interface between the Ras-like and helical domains 3 .Our NMR, biophysical and computational analyses indicate that changes in the ionization state of residues within the pH sensing network promote a disorder-to-order transition in Gi over the physiological pH range, to populate a less ordered state that enhances Gαi-Gβγ association in HEK293 cells at the lower end of the physiological pH range.
Identification of the pH-sensing network allowed for the identification and generation of low pH mimetics that reduce pH-dependent Gβγ release from the Gαi-Gβγ complex.Of note, the Gi Switch III region plays a key role in pH sensing, providing a new role of this understudied Switch region in agonist-mediated Gβγ release, a key important step for both Gαi and Gβγ activation.
Taken together, our studies indicate that Gαi undergoes a pH-dependent order-to-disorder transition that modulates Gαi-Gβγ interactions and Gαi activation over the physiological pH range.

Site-directed mutagenesis
Gαi variants were generated using the Q5 Site-Directed Mutagenesis kit (NEB).Polymerase chain reaction (PCR) primers were designed using the NEBaseChanger (https://nebasechanger.neb.com), an online mutagenesis primer design tool powered by New England Biolabs from Eton Bioscience Inc. Mutagenesis was performed as described 4 .The variant constructs were sequenced by Sanger Sequencing (Genewiz) to confirm successful mutagenesis.
Sequencing results for Gαi variants were aligned with published sequences of wildtype (WT) Gαi using Clustal Omega (European Molecular Biology Laboratory, Cambridgeshire, UK).

Expression and purification of WT Gαi and its variants
Gαi proteins were overexpressed in E. coli Agilent BL21-codon plus (DE3)-RP-X competent cells.
Cells were grown at 37˚C for 3-4 hours to achieve an optical density at 600 nm (OD 600) of 0.60 and then induced with 500 µM isopropylthio-β-galactoside (IPTG).The temperature was then reduced to 18˚C and the cultures were left to grow overnight.After 20 hours, bacteria cells were harvested by centrifugation at 13,000 rpm and then resuspended in 50 mL Dialysis buffer (  9 .

Intrinsic tryptophan fluorescence
Intrinsic tryptophan fluorescence assays were conducted using 2 M purified WT or variant GDPloaded Gαi proteins in 200 l of assay buffer (20 mM HEPES, 50 mM NaCl, 5 mM MgCl2, 2 mM DTT) at different pH (pH 5, 6 and 7.2) in the 96 well plate.G proteins were excited at 290 nm and intrinsic tryptophan fluorescence was measured from 300 to 400 nm wavelength using a SpectraMax M4 Series Microplate Reader.
2D NMR 1 H-15 N HSQC spectra of Gαi were acquired on a Bruker Avance 850 MHz (14.1 T field strength) NMR spectrometer at 25 °C, with a cryogenic (TCI) 5 mm triple resonance probe equipped with a z-axis gradient.The 1 H-15 N HSQC spectra of Gαi-GDP were assigned using a combination of triple resonance experiments, including 3D HNCA, HN(CO)CA, HN(CA)CO and HNCO 7 .TROSY-based pulse sequences were used for sensitivity enhancement.Bruker-TopSpin was used to process the NMR data and NMRFAM-SPARKY was used to visualize and analyze the NMR spectra 10 .For assignments, BMRB 30078 was used as reference spectrum for Gαi-GDP 11 .pH titration studies were performed by calculating the chemical shift perturbation (CSP) of Gαi-GDP over a pH range from 6.4, 6.8, 7.0, 7.2, 7.4 and 7.6.Average 1 H-15 N CSP were determined using the formula Δδ = [(Δ 1 HN) 2 + (Δ 15 N/5) 2 ] 0.5 , as previously described 12 .PyMOL (https://pymol.org/2/)was used to generate all images of molecular structures.
After collecting data for a few seconds to obtain baseline fluorescence, purified 1 μM Gαi-GDP was added to the solution to initiate GDP association.Once the fluorescence reached saturation, 10 x (7.5 µM) GDP was added to the solution to initiate the GDP dissociation.

Molecular Dynamics (MD) simulations
Gαi-GDP coordinates were extracted from the Gαi-Gβγ complex structure (PDB: 1GP2) to visualize Switch residues not observable in Gαi-GDP structures 13 .Modeller 9v21.2 was employed to relocate missing residues and generate charged and uncharged states for residues E236, D237 and E245 14 to represent the charged state as higher pH (above 7.2) and the uncharged state as low pH (below 6.4).Following side-chain optimizations, minimum energy conformations of charged and uncharged GDP-bound Gi states were identified.Subsequently, MD simulations were performed to investigate conformational and dynamic differences compared to WT GDP-bound Gαi.The CHARMM36 forcefield was used to parametrize the protein and GDP, and simulations were run using GROMACS-2020.3.5 15 .Gαi proteins were solvated in a water cubic box containing approximately 22,500 TIP3P water molecules and maintained a salt concentration of 150 mM through the addition of an appropriate number of Na+ and Cl-ions.The solvated system was energy minimized using the conjugate gradient algorithm and subsequently equilibrated using the V-rescale thermostat and the Parrinello-Rahman barostat 16 .Long-range electrostatic interactions were evaluated using the Particle-Mesh Ewald sum 17 , and all bonds involving hydrogen atoms were constrained using the LINCS algorithm 18 .Simulations were run for 250 ns and were repeated in triplicate to ensure reproducibility and maintain consistency.Structural figures were generated using PyMOL and graphical plots were created using Grace (http://plasmagate.weizmann.ac.il/Grace/).

HADDOCK docking
The HADDOCK online server was used for the computational docking of Gαi with Gβγ 19 .The initial structure of Gαi with E236, D237, and E245 residue side chains in either the charged (higher pH) or uncharged state (lower pH) as described above, was obtained from the averaged structure of 250 ns MD simulations.The Gβγ structure was extracted from the Gαi-Gβγ complex crystal structure (PDB:1GP2).Contact residues between Gαi and Gβγ that form the trimeric complex structure were used as active residues and the passive residues were auto selected by the software.
The docking model corresponding to the lowest energy was used for further analysis.

Intracellular pH modulation:
The intracellular pH of HEK293 cells was altered by two different methods.In the first method, HEK293 cells were treated with 1-5 µM of trifluoromethoxycarbonylcyanide phenylhydrazone (FCCP) for 5 min.In the second method, intracellular pH was modulated by incubating HEK293 cells with Hanks' Balanced Salt Solution (HBSS) buffer at different pH for 15 min.Intracellular pH was quantified using the intracellular pH indicator, 2',7'-bis-(2-carboxyethyl)-5-(and-6)carboxyfluorescein-acetoxymethyl ester (BCECF-AM) as described 20 .To convert BCECF-AM fluorescence to the intracellular pH values, HEK293 cells were treated with 20 µM nigericin and a calibration curve for pHi and BCECF-AM fluorescence was generated.
One day after seeding in 96-well assay plates, white backings (Perkin Elmer, Waltham, MA) were applied to the plate bottoms, and the growth medium was aspirated.Cells were washed three times with Assay buffer (1X HBSS, 20 mM HEPES, pH 7.4), then 60 μL of Assay buffer was added immediately, followed by a 10 μL addition of freshly prepared 50 μM coelenterazine 400a (1bisdeoxycoelenterazine) (Nanolight Technologies, Pinetop, AZ) in each well.After a five-minute equilibration period, cells were treated with 30 μL of 3x neurotensin (3 x 10 -5 M) for another 5 minutes.Plates were then read in a plate reader (Clariostar, BMG Labtech, Ortenberg, Germany) at 395 nm (Rluc8-coelenterazine 400a) and 510 nm (GFP2) with a slit width of 8 nm using 10 flashes per spiral well scan.Plates were read serially five times, and measurements from the third read were used in all analyses.BRET ratios were computed as the ratio of the GFP2 emission to Rluc8 emission.

Statistical Analysis
CD melt-curves were fit to Boltzmann sigmoidal equation in Prism 9.3.1 (GraphPad Software, San Diego, CA).Concentration-response curves for BRET assays were fit to a three-parameter logistic equation.Raw BRET concentration-response curves were normalized to the best-fit maximum within a data set.BRET data were represented as mean ± SEM.Data analysis was carried out using Prism 9.3.1 (GraphPad Software, San Diego, CA).

pH modulation of GDP-bound Gαi structure and dynamic properties
Earlier investigations into mammalian Gαi demonstrated changes in structure and stability in response to pH variations 8 .While these findings suggest a potential role for Gαi as a pH sensor 8 , the molecular basis and functional relevance has yet to be established.Herein, we apply comprehensive and multidisciplinary approaches to evaluate how pH changes in the physiological range affect structure, stability and Gi1 activity in vitro and in cells.We first conducted CD experiments on GDP-bound Gαi to monitor pH-dependent changes in thermal unfolding, stability and secondary structure.Further, to monitor thermal unfolding and stability, we collected CD spectra at 222 nm as a function of pH and temperature.As shown in Fig. 1A, a striking and gradual increase in thermal stability (Tm ~ 25 o C) of Gαi-GDP was observed over the pH range from 5 to 7.3.Notably, the thermal unfolding transition for Gαi-GDP appears cooperative at low pH but shifts to a multi-state unfolding transition over the physiological pH range (Fig. 1A).Gαi contains two subdomains, a Ras-like and helical domain.To evaluate differential unfolding at higher pH (above pH 7.1), we conducted CD scans (200-250 nm) for Gαi-GDP as a function of temperature at pH 7.2.As shown in Fig. 1B and C, CD spectra revealed a significant reduction in alpha-helical propensity, but not the beta-sheet propensity as the temperature is raised from 45-55°C.These findings indicate that the helical domain melts first, followed by the Ras-like domain, with the Ras-like domain showing the greatest change in thermal stability at higher pH.Given the enhanced stability observed at higher pH, we employed intrinsic tryptophan fluorescence experiments using tryptophan (W211) in the SW-II region (Fig. 3A) as a fluorescence probe to monitor differences in solvent exposure as a function of pH which indirectly indicates pH-dependent switch conformational changes.It has previously been shown that the more dynamic and less ordered GDP-bound state of Gαi promotes enhanced exposure of W211 resulting in a fluorescence decrease relative to that of the Gαi-GTP state 7,21 .As shown in Fig. 1D, intrinsic Gαi-GDP tryptophan fluorescence increases over the pH range from 5 to 7.2, suggesting that higher pH promotes decreased solvent accessibility possibly due to enhanced interactions and structural order, consistent with the greatly enhanced stability observed by CD.Taken together, these results support a disorder-to-order transition at higher physiological pH for Gαi in its GDP-bound form.
To further probe whether a pH-dependent disorder-to-order transition occurs in GDPbound Gαi, we conducted 2D NMR analyses.For these studies, we prepared 15 N enriched Gαi-GDP and collected a 2D 1 H-15 N Heteronuclear Single Quantum Coherence (HSQC) NMR spectra over a physiologically relevant pH range (6.4 -7.6).Enrichment with 15 N allows the detection of backbone and sidechain NH resonances within Gαi and provides a site-specific probe for every amino acid except proline.The 2D HSQC overlay of Gαi-GDP at pH 6.4, 6.8 and 7.2 is shown in Fig. 2A, with an HSQC overlay comparing pH 6.4 versus pH 7.6 shown separately in Supplementary Fig. S1.Spectra acquired at lower pH show significant chemical shift changes, line broadening and loss of several peaks in comparison to those obtained at higher pH values, suggesting the protein is more dynamic at lower pH (Fig. 2A).This data correlates well with CD and tryptophan fluorescence data which suggests a less thermostable structure of Gαi-GDP at lower pH.The pH-dependent HSQC changes observed are consistent with an earlier report which showed extensive broadening of NH peaks in the 1 H-15 N HSQC spectrum of Gαi-GDP at pH 6 relative to pH 7 22 .

pH-dependence of GDP binding to Gαi
Gαi when bound to GDP, adopts a conformational ensemble and dynamic properties distinct from that of the GTP-bound state 7 .This in turn drives recognition of regulatory factors and downstream targets.Given our findings that pH modulates Gαi-GDP structure, stability and dynamics, we asked if pH could alter GDP binding to Gαi protein.For that purpose, we performed Mant-GDP dissociation assays.For these assays, the rate of GDP dissociation was determined by adding excess GDP to Mant-GDP loaded Gαi and monitoring Mant fluorescence changes (by FRET upon tryptophan excitation) as a function of time at different pH values (Fig. 3A-B).As shown in Fig. 3C-D, small differences in GDP dissociation rates were observed over the pH range (pH 6.8-7.4), indicating that GDP binding is not significantly altered at physiological pH.

Identification and characterization of pH sensing residues of Gαi-GDP
To elucidate the molecular basis for pH-dependent stability and conformational dynamic changes in GDP-bound Gαi, we sought to identify key residues involved in pH sensing.As extensive broadening of resonances in GDP-bound spectra prevented pKa determination by NMR, we examined and identified two networks of charged residues in regions, designated as the 'GDP release network' (Fig. 4A) and 'Switch network' (Fig. 5A), that showed pH-dependent changes in the NMR spectra (Fig. 2D).The GDP release network contains residues within α1, α5, β2, and β3, and was previously shown to be important for GDP release during the GPCR-mediated activation of the Gαi 23 .We postulated that if this network plays a key role in pH sensing, mutation of charged residues within this network (e.g., H57, H188, K192, D193, H195 and D337) would reduce pHdependent Gαi thermostability, due to protonation and loss of electrostatic interactions that destabilize Gi tertiary structure.To examine whether this network modulates pH-dependent changes in stability and nucleotide binding, we generated several Gαi variants and conducted pH-dependent CD thermal melt and nucleotide dissociation assays.To select neutral or uncharged amino acid substitutions that retain or have minimal effect on Gαi structure, we employed the Rosetta modeling suite.Rosetta replaces a desired amino acid within the protein to all possible amino acid substitutions and predicts associated free energy changes 24 .Substitutions that minimally perturb free energy are predicted to retain protein structure.Using this strategy, we identified four variants (H57T, H188V, K192Q and H195N) predicted to retain Gαi structure and stability.To evaluate pH-dependent thermostability associated with these Gαi GDP release network variants, we acquired CD thermal melts at pH 6 and 7.2, as shown in Fig. 4. Of note, all four variants retained pH-dependent thermostability similar to WT Gαi (data not shown for H188 and H195) (Fig. 4C-E).We also performed GDP dissociation assays to evaluate whether the variants alter nucleotide binding (Fig. 4B).All variants showed pH-dependent GDP dissociation rates similar to WT Gαi.These findings indicate that the GDP release network does not significantly modulate pH-dependent stability or nucleotide binding (Fig. 4B-F).Gαi release network variants retain pH-dependent thermal unfolding profiles similar to WT Gαi.
As a number of charged residues within the Switch regions form stabilizing electrostatic interactions in the active Gαi-GTP bound state, we next postulated that the decreased stability of Gαi-GDP at lower pH may result from protonation of pH-dependent Switch network (SW-I, SW-II, SW-III and α3) residues.To test this hypothesis, we selected residues from the Switch network shown or predicted to be important for Switch stability in the GTP-bound form of Gαi 25 .Then, based on Rosetta prediction, we mutated a subset of charged residues within this network (e.g.R205N, R208Q, E236L, D237G, R242Q and E245N) predicted to least perturb Gαi structure (Fig. 5A).As shown in Fig. 5B-E, two of the variants located in SW-III (E236L and D237G) and α3 (E245N), respectively, showed a reduction in pH-dependent thermostability between pH 6 and 7.2 relative to WT Gαi.Moreover, a 'double variant' consisting of two substitutions (E236L and E237G) from SW-III showed further reduction in pH-induced stability but not complete abolishment of pH dependence (Fig. 5F).Yet, a 'triple variant' containing all three substitutions (Gαi E236L, D237G and E245N) effectively eliminated pH-induced stability changes relative to WT Gαi (Fig. 5G), suggesting a key role for these three Switch network residues (E236, D237 and E245) in stabilizing Gαi at higher pH.Other variants from the Switch network at R205 and R208 did not show any change in pH-dependent thermostability in comparison to WT Gαi protein (Supplementary Fig. S2).We also monitored tryptophan fluorescence of the GDP-bound Gi triple variant as a function of pH to examine changes in solvent accessibility of SW-II residue W211.Consistent with the loss in pH-dependent thermal stability, we found that the double variant and triple variant reduce and abolish (Supplementary Fig. S3) pH-dependent fluorescence intensity changes relative to WT Gαi-GDP, respectively (Fig. 5H).As the CD thermal and fluorescence profiles associated with the double variant and triple variant mimic WT Gαi at lower pH (pH 6), we refer to these variants as "low pH mimetics".Further to confirm that the identified residues participate in pH sensing, we performed NMR analyses for the double variant at pH 6.4 and pH 7.2 in comparison to WT GDP-bound Gi.As shown in Supplementary Fig. S4, pHdependent changes in peak intensity and chemical shift perturbations are significantly reduced in the double variant with respect to WT Gαi.Taken together, our results point to key residues in the Switch network that regulate pH-dependent stability and conformational dynamic properties.

MD simulations of pH-dependent Gαi-GDP conformational dynamics
We identified three residues in the GDP-bound Gi, including two SW-III residues (E236, D237) and one α3 residue (E245), that appear to play a key role in pH-dependent stability and conformational dynamics.To evaluate how these residues form pH-dependent electrostatic interactions that stabilize the Switch regions at higher pH, we employed MD simulations.We generated Gαi structures using the Gαi-GDP crystal structure (PDB: 1GP2) as a starting point and then changed the protonation state of side chains associated with residues E236, D237 and E245 to simulate a low pH and high pH state, respectively.Representative snapshots extracted from the MD trajectories of Gαi-GDP provide insights into the dynamic behavior of Gαi as a function of pH.In the charged or higher pH state of Gαi-GDP, we observe the transient formation of three critical salt-bridge interactions: E236 interacting with R205, E245 with R208, and E245 with K248 (Fig. 6D and F).Notably, these residues form electrostatic interactions as observed in crystal structures of active Gαi-GTP state (PDB:1CIP) and play a pivotal role in the stabilization of the Switch regions.In particular, E236 and D237 side chains from SW-III interact with R205 and R208 in SW-II whereas α3 residue E245 interacts with R208 25 .In the uncharged state of Gαi-GDP (lower pH), conformations extracted from MD simulation trajectories exhibit a notable absence of these salt-bridge interactions (Fig. 6E), which we attribute to the destabilization of the SW-III and SW-II region (Fig. 6E).As a result, the protein displays increased dynamics, as evidenced by higher RMSD and RMSF values, suggesting greater structural fluctuations.The protonation of key residues interferes with the formation of electrostatic interactions leading to their destabilization.
Consequently, the protein becomes more dynamic and less thermally stable under more acidic conditions.

Fig. 2 :
Fig. 2: Gαi-GDP 1 H-15 N 2D NMR spectral changes as a function of pH.(A) Representative 2D 1 H-15 N TROSY-HSQC spectral overlay of 2 H, 13 C, 15 N-enriched Gαi-GDP (230 µM) at pH 6.4, 6.8, and 7.2, highlighting peak shifts and line broadening.Spectra were acquired on a Bruker Avance III 850 MHz instrument at 25 °C (N = 2).(B) Chemical shift perturbation (CSP) and (C) peak intensity ratio (IntpH 6.4/IntpH 7.2) for pH 6.4 and 7.2.The striped horizontal line represents the mean value of Δδ amide.Most of the residues that show significant CSP, and broadening lie within the Ras-like domain (α1, α5, β1, β2 and β2-β3 loop).(D) Spectral differences are highlighted on a ribbon diagram of GTPbound Gαi (PDB: 1CIP).NH residues with CSP greater than 0.03 ppm are represented in red.Residues with decreased peak intensity (line broadening) at pH 6.4 relative to pH 7.2 are shown in green.Residues missing or unassigned are shown in black color while unaffected residues are shown in blue color.Residue-specific chemical shift perturbation and peak intensity changes associated with changes in pH between 6.4 and 7.2 are plotted in Fig.2B and C, respectively.Most pH-dependent spectral changes are localized to α1, α5, β1, β2 and the β2-β3 loop within the Ras-like domain as mapped

Fig. 3 :
Fig. 3: Rates of Gαi GDP nucleotide dissociation vary with pH.(A) Structure of Gαi (PDB:1GP2) highlighting the position of tryptophan (W211) in Switch II and bound Mant-GDP.(B) Diagram displaying experimental setup of FRET-based determination of Gαi nucleotide association and dissociation rates.(C) Rate of GDP dissociation from Gαi as a function of pH by monitoring the time-dependent decrease in FRET emission of Gαi-loaded Mant-GDP at 445 nm upon the addition of 7.5 µM GDP.(D) The rate of Mant-GDP dissociation decreases as the pH increases.Data are averages of two independent experiments performed in duplicate (±SE).

Fig. 4 :
Fig. 4: pH-dependent nucleotide exchange and stability of Gαi variants from the GDP release network.(A) Ribbon diagram of GTP-bound Gαi (PDB: 1CIP) highlighting electrostatic network near GDP release network.The helical domain, Ras-like domain and the Switch regions are shown in gray, blue, and green, respectively.Charged residues are shown as yellow sticks.(B) Rates of GDP dissociation is compared for WT Gαi and Gαi variants (H57T and K192Q) as a function of pH.Data are averages of two independent experiments performed in duplicate (±SE) (C) Representative CD melt profile of GDP-bound WT and variant Gαi (N=2), Gαi H57T (D) and K192Q (E) proteins.

Fig. 5 .
Fig. 5. pH-dependent stability and tryptophan fluorescence of Gαi Switch network variants.(A) The putative pH-dependent electrostatic network (red) within the Switch regions (green) is highlighted on the ribbon diagram of GTP-bound Gαi (PDB: 1CIP).(B) Comparison of representative CD melt profiles obtained from two independent experiments at pH 6.0 and 7.2 for WT Gαi-GDP, (C) Gαi-GDP E245N, (D) Gαi-GDP E236L, (E) Gαi-GDP D237G (F) Gαi-GDP double variant (E236L/D237G), and (G) a Gαi-GDP triple variant (E236L/D237G/E245Q).The CD thermal profile for Gαi single and double variants shows decreased pH-dependent thermal stability while the triple variant shows a complete loss of pH-dependent thermal stability compared to WT Gαi.(H) Representative intrinsic tryptophan fluorescence spectra of Gαi-GDP triple variant (2 µM, excitation = 280 nm, emission = 300 -400 nm) as a function of pH (N = 2, performed in triplicate).Consistent with the CD results, pH-dependent intrinsic tryptophan (W211) fluorescence observed for WT Gαi-GDP is abolished for the Gαi triple variant supporting a role for E236, D237 and E245 in pH-dependent stability and structural changes.Analysis of MD simulation trajectories of GDP-bound Gi collected for 250 ns in charged versus uncharged states revealed higher root mean square deviation (RMSD) in the protonated or